System, Method, and Computer Program Product for Operating Dynamic Shadow Testing Environments

ABSTRACT

Described are a system, method, and computer program product for operating dynamic shadow testing environments for machine-learning models. The method includes storing a testing policy including an identifier of a machine-learning model and an identifier of a transaction service. The method includes generating a shadow testing environment operating the transaction service using the machine-learning model. The method also includes receiving, at a transaction service provider system, a transaction authorization request including transaction data of a transaction associated with a payment device. The method further includes identifying the machine-learning model associated with the transaction based on a parameter of the transaction data. The method further includes determining, based on the identifier of the machine-learning model, the testing policy and the shadow testing environment. The method further includes replicating the transaction data in the shadow testing environment as input for testing the transaction service using the machine-learning model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/658,840, filed Oct. 21, 2019, entitled “System, Method, and ComputerProgram Product for Operating Dynamic Shadow Testing Environments,” thedisclosure of which is hereby incorporated by reference in its entirety.

BACKGROUND 1. Technical Field

Disclosed embodiments or aspects relate generally to networked computerservice analysis, and, in one particular embodiment or aspect, to asystem, method, and computer program product for operating dynamicshadow testing environments replicating live data for services usingmachine-learning models.

2. Technical Considerations

Testing a computer service prior to live deployment on production datacan be a difficult process. Duplicating a historic set of data may notproperly replicate the flow and timing that certain computer servicesmay require from a stream of live data. Conversely, testing computerservices on live data may create unnecessary risk from accidental dataoverwriting or triggered service actions. Creating a test environmentthat mimics the flow of live data can be a very labor-intensive, manualprocess of setup, resource allocation, and generation. Even after a testenvironment is established, iterative testing of a service remains adhoc, leading to inefficient redeployment after a service or underlyingmodel is modified. In short, operating test environments for servicesthat are to be deployed on live data is a slow, inefficient, andinelastic process relative to computer processing requirements, dataintegrity, and testing accuracy. The deficiencies of live data servicetesting is particularly apparent for electronic payment processingnetworks that require extensive and thorough testing of transactionservices prior to deployment.

There is a need in the art for a technical solution that dynamicallygenerates and operates shadow testing environments for live data,particularly live transaction data processed at a transaction serviceprovider system. There is a need for such a technical solution toprovide automatic replication of data in relevant shadow testingenvironments, where testing policies can be set and modified duringruntime, with efficient identification and allocation of computerresources.

SUMMARY

Accordingly, and generally, provided is an improved system, method, andcomputer program product for operating dynamic shadow testingenvironments. The method may include storing a testing policy includingidentifiers of a machine-learning model and a transaction service. Themethod may also include generating a shadow testing environmentoperating the transaction service using the machine-learning model andreceiving a transaction authorization request including transactiondata. The method may further include identifying the machine-learningmodel associated with the transaction based on a parameter of thetransaction data and determining, based on the identifier of themachine-learning model, the shadow testing environment. The method mayfurther include replicating the transaction data in the shadow testingenvironment as input for testing the transaction service using themachine-learning model.

According to non-limiting embodiments or aspects, provided is acomputer-implemented method for operating dynamic shadow testingenvironments. The method may include storing, with at least oneprocessor and in a database, at least one testing policy including anidentifier of at least one machine-learning model and an identifier ofat least one transaction service. The method may also includegenerating, with at least one processor, at least one shadow testingenvironment operating the at least one transaction service using the atleast one machine-learning model. The method may further includereceiving, with at least one processor at a transaction service providersystem, at least one transaction authorization request includingtransaction data of at least one transaction associated with at leastone payment device. The method may further include identifying, with atleast one processor, the at least one transaction service associatedwith the at least one transaction based on a parameter of thetransaction data. The method may further include determining, with atleast one processor, the at least one shadow testing environmentassociated with the at least one transaction service. The method mayfurther include replicating, with at least one processor, thetransaction data in the at least one shadow testing environment as inputfor testing the at least one transaction service using the at least onemachine-learning model.

In some non-limiting embodiments or aspects, the method may includedetecting, with at least one processor, a modification of the at leastone testing policy by a user through a computer interface. The methodmay also include determining, with at least one processor, that themodification includes a new identifier of at least one newmachine-learning model and/or a new identifier of at least one newtransaction service. The method may further include regenerating, withat least one processor, the at least one shadow testing environment tooperate the at least one new transaction service and/or use the at leastone new machine-learning model.

In some non-limiting embodiments or aspects, the method may include, inresponse to determining that the at least one new transaction serviceand/or the at least one new machine-learning model has not yet beenstored, prompting, with at least one processor, the user through thecomputer interface to provide the at least one new transaction serviceand/or the at least one new machine-learning model.

In some non-limiting embodiments or aspects, the method may includereceiving, with at least one processor at a transaction service providersystem, a plurality of transaction authorization requests including theat least one transaction authorization request. The method may alsoinclude replicating, with at least one processor in a respective shadowtesting environment of the at least one shadow testing environment, eachof the plurality of transaction authorization requests that isassociated with a transaction service of the at least one testingpolicy.

In some non-limiting embodiments or aspects, replication of a giventransaction authorization request occurs in real-time with processing ofthe given transaction authorization request by the transaction serviceprovider system.

In some non-limiting embodiments or aspects, generating the at least oneshadow testing environment may include identifying, with at least oneprocessor, a set of computer resources available for operating shadowtesting environments. Generating the at least one shadow testingenvironment may also include determining, with at least one processor, aresource requirement of the at least one testing policy. Generating theat least one shadow testing environment may further include selecting,with at least one processor based at least partly on the resourcerequirement, a subset of computer resources to operate the at least oneshadow testing environment. Generating the at least one shadow testingenvironment may further include initiating, with at least one processor,the at least one shadow testing environment using the subset of computerresources.

In some non-limiting embodiments or aspects, the method may include, inresponse to detecting a modification of the at least one testing policy,determining, with at least one processor and based on the modification,a new resource requirement of the at least one testing policy. Themethod may also include selecting, with at least one processor based atleast partly on the new resource requirement, a new subset of computerresources to operate the at least one shadow testing environment. Themethod may further include initiating, with at least one processor, theat least one shadow testing environment using the new subset of computerresources.

According to non-limiting embodiments or aspects, provided is a systemoperating dynamic shadow testing environments. The system may include aserver including at least one processor. The server may be programmedand/or configured to store, in a database, at least one testing policyincluding an identifier of at least one machine-learning model and anidentifier of at least one transaction service. The server may also beprogrammed and/or configured to generate at least one shadow testingenvironment operating the at least one transaction service using the atleast one machine-learning model. The server may further be programmedand/or configured to receive, at a transaction service provider system,at least one transaction authorization request including transactiondata of at least one transaction associated with at least one paymentdevice. The server may further be programmed and/or configured toidentify the at least one transaction service associated with the atleast one transaction based on a parameter of the transaction data. Theserver may also be programmed and/or configured to determine the atleast one shadow testing environment associated with the at least onetransaction service. The server may also be programmed and/or configuredto replicate the transaction data in the at least one shadow testingenvironment as input for testing the at least one transaction serviceusing the at least one machine-learning model.

In some non-limiting embodiments or aspects, the server may be furtherprogrammed and/or configured to detect a modification of the at leastone testing policy by a user through a computer interface. The servermay also be further programmed and/or configured to determine that themodification includes a new identifier of at least one newmachine-learning model and/or a new identifier of at least one newtransaction service. The server may be further programmed and/orconfigured to regenerate the at least one shadow testing environment tooperate the at least one new transaction service and/or use the at leastone new machine-learning model.

In some non-limiting embodiments or aspects, the server may be furtherprogrammed and/or configured to, in response to determining that the atleast one new transaction service and/or the at least one newmachine-learning model has not yet been stored, prompt the user throughthe computer interface to provide the at least one new transactionservice and/or the at least one new machine-learning model.

In some non-limiting embodiments or aspects, the server may beprogrammed and/or configured to receive, at a transaction serviceprovider system, a plurality of transaction authorization requestsincluding the at least one transaction authorization request. The servermay also be further programmed and/or configured to replicate, in arespective shadow testing environment of the at least one shadow testingenvironment, each of the plurality of transaction authorization requeststhat is associated with a transaction service of the at least onetesting policy.

In some non-limiting embodiments or aspects, replication of a giventransaction authorization request occurs in real-time with processing ofthe given transaction authorization request by the transaction serviceprovider system.

In some non-limiting embodiments or aspects, generating the at least oneshadow testing environment may include identifying a set of computerresources available for operating shadow testing environments.Generating the at least one shadow testing environment may also includedetermining a resource requirement of the at least one testing policy.Generating the at least one shadow testing environment may furtherinclude selecting, based at least partly on the resource requirement, asubset of computer resources to operate the at least one shadow testingenvironment. Generating the at least one shadow testing environment mayfurther include initiating the at least one shadow testing environmentusing the subset of computer resources.

In some non-limiting embodiments or aspects, the server may beprogrammed and/or configured to, in response to detecting a modificationof the at least one testing policy, determine, based on themodification, a new resource requirement of the at least one testingpolicy. The server may also be programmed and/or configured to select,based at least partly on the new resource requirement, a new subset ofcomputer resources to operate the at least one shadow testingenvironment. The server may further be programmed and/or configured toinitiate the at least one shadow testing environment using the newsubset of computer resources.

According to non-limiting embodiments or aspects, provided is a computerprogram product for operating dynamic shadow testing environments. Thecomputer program product may include at least one non-transitorycomputer-readable medium including program instructions that, whenexecuted by at least one processor, cause the at least one processor tostore, in a database, at least one testing policy including anidentifier of at least one machine-learning model and an identifier ofat least one transaction service. The program instructions may furthercause the at least one processor to generate at least one shadow testingenvironment operating the at least one transaction service using the atleast one machine-learning model. The program instructions may furthercause the at least one processor to receive, at a transaction serviceprovider system, at least one transaction authorization requestincluding transaction data of at least one transaction associated withat least one payment device. The program instructions may further causethe at least one processor to identify the at least one transactionservice associated with the at least one transaction based on aparameter of the transaction data. The program instructions may furthercause the at least one processor to determine the at least one shadowtesting environment associated with the at least one transactionservice. The program instructions may further cause the at least oneprocessor to replicate the transaction data in the at least one shadowtesting environment as input for testing the at least one transactionservice using the at least one machine-learning model.

In some non-limiting embodiments or aspects, the program instructionsmay cause the at least one processor to detect a modification of the atleast one testing policy by a user through a computer interface. Theprogram instructions may also cause the at least one processor todetermine that the modification includes a new identifier of at leastone new machine-learning model and/or a new identifier of at least onenew transaction service. The program instructions may further cause theat least one processor to regenerate the at least one shadow testingenvironment to operate the at least one new transaction service and/oruse the at least one new machine-learning model.

In some non-limiting embodiments or aspects, the program instructionsfurther may cause the at least one processor to, in response todetermining that the at least one new transaction service and/or the atleast one new machine-learning model has not yet been stored, prompt theuser through the computer interface to provide the at least one newtransaction service and/or the at least one new machine-learning model.

In some non-limiting embodiments or aspects, the program instructionsmay cause the at least one processor to receive, at a transactionservice provider system, a plurality of transaction authorizationrequests including the at least one transaction authorization request.The program instructions may also cause the at least one processor toreplicate, in a respective shadow testing environment of the at leastone shadow testing environment, each of the plurality of transactionauthorization requests that is associated with a transaction service ofthe at least one testing policy.

In some non-limiting embodiments or aspects, generating the at least oneshadow testing environment may include identifying a set of computerresources available for operating shadow testing environments.Generating the at least one shadow testing environment may also includedetermining a resource requirement of the at least one testing policy.Generating the at least one shadow testing environment may furtherinclude selecting, based at least partly on the resource requirement, asubset of computer resources to operate the at least one shadow testingenvironment. Generating the at least one shadow testing environment mayfurther include initiating the at least one shadow testing environmentusing the subset of computer resources.

In some non-limiting embodiments or aspects, the program instructionsmay cause the at least one processor to, in response to detecting amodification of the at least one testing policy, determine, based on themodification, a new resource requirement of the at least one testingpolicy. The program instructions may also cause the at least oneprocessor to select, based at least partly on the new resourcerequirement, a new subset of computer resources to operate the at leastone shadow testing environment. The program instructions may also causethe at least one processor to initiate the at least one shadow testingenvironment using the new subset of computer resources.

Other non-limiting embodiments or aspects of the present disclosure willbe set forth in the following numbered clauses:

Clause 1: A computer-implemented method comprising: storing, with atleast one processor and in a database, at least one testing policycomprising an identifier of at least one machine-learning model and anidentifier of at least one transaction service; generating, with atleast one processor, at least one shadow testing environment operatingthe at least one transaction service using the at least onemachine-learning model; receiving, with at least one processor at atransaction service provider system, at least one transactionauthorization request comprising transaction data of at least onetransaction associated with at least one payment device; identifying,with at least one processor, the at least one transaction serviceassociated with the at least one transaction based on a parameter of thetransaction data; determining, with at least one processor, the at leastone shadow testing environment associated with the at least onetransaction service; and replicating, with at least one processor, thetransaction data in the at least one shadow testing environment as inputfor testing the at least one transaction service using the at least onemachine-learning model.

Clause 2: The method of clause 1, further comprising: detecting, with atleast one processor, a modification of the at least one testing policyby a user through a computer interface; determining, with at least oneprocessor, that the modification comprises a new identifier of at leastone new machine-learning model and/or a new identifier of at least onenew transaction service; and regenerating, with at least one processor,the at least one shadow testing environment to operate the at least onenew transaction service and/or use the at least one new machine-learningmodel.

Clause 3: The method of Clause 1 or 2, further comprising, in responseto determining that the at least one new transaction service and/or theat least one new machine-learning model has not yet been stored,prompting, with at least one processor, the user through the computerinterface to provide the at least one new transaction service and/or theat least one new machine-learning model.

Clause 4: The method of any of Clause 1-3, further comprising:receiving, with at least one processor at the transaction serviceprovider system, a plurality of transaction authorization requestscomprising the at least one transaction authorization request; andreplicating, with at least one processor in a respective shadow testingenvironment of the at least one shadow testing environment, each of theplurality of transaction authorization requests that is associated witha transaction service of the at least one testing policy.

Clause 5: The method of any of Clause 1-4, wherein replication of agiven transaction authorization request occurs in real-time withprocessing of the given transaction authorization request by thetransaction service provider system.

Clause 6: The method of any of Clause 1-5, wherein generating the atleast one shadow testing environment further comprises: identifying,with at least one processor, a set of computer resources available foroperating shadow testing environments; determining, with at least oneprocessor, a resource requirement of the at least one testing policy;selecting, with at least one processor based at least partly on theresource requirement, a subset of computer resources to operate the atleast one shadow testing environment; and initiating, with at least oneprocessor, the at least one shadow testing environment using the subsetof computer resources.

Clause 7: The method of any of Clause 1-6, further comprising, inresponse to detecting a modification of the at least one testing policy:determining, with at least one processor and based on the modification,a new resource requirement of the at least one testing policy;selecting, with at least one processor based at least partly on the newresource requirement, a new subset of computer resources to operate theat least one shadow testing environment; and initiating, with at leastone processor, the at least one shadow testing environment using the newsubset of computer resources.

Clause 8: A system comprising a server comprising at least oneprocessor, the server being programmed and/or configured to: store, in adatabase, at least one testing policy comprising an identifier of atleast one machine-learning model and an identifier of at least onetransaction service; generate at least one shadow testing environmentoperating the at least one transaction service using the at least onemachine-learning model; receive, at a transaction service providersystem, at least one transaction authorization request comprisingtransaction data of at least one transaction associated with at leastone payment device; identify the at least one transaction serviceassociated with the at least one transaction based on a parameter of thetransaction data; determine the at least one shadow testing environmentassociated with the at least one transaction service; and replicate thetransaction data in the at least one shadow testing environment as inputfor testing the at least one transaction service using the at least onemachine-learning model.

Clause 9: The system of Clause 8, wherein the server is furtherprogrammed and/or configured to: detect a modification of the at leastone testing policy by a user through a computer interface; determinethat the modification comprises a new identifier of at least one newmachine-learning model and/or a new identifier of at least one newtransaction service; and regenerate the at least one shadow testingenvironment to operate the at least one new transaction service and/oruse the at least one new machine-learning model.

Clause 10: The system of Clause 8 or 9, wherein the server is furtherprogrammed and/or configured to, in response to determining that the atleast one new transaction service and/or the at least one newmachine-learning model has not yet been stored, prompt the user throughthe computer interface to provide the at least one new transactionservice and/or the at least one new machine-learning model.

Clause 11: The system of any of Clause 8-10, wherein the server isfurther programmed and/or configured to: receive, at the transactionservice provider system, a plurality of transaction authorizationrequests comprising the at least one transaction authorization request;and replicate, in a respective shadow testing environment of the atleast one shadow testing environment, each of the plurality oftransaction authorization requests that is associated with a transactionservice of the at least one testing policy.

Clause 12: The system of any of Clause 8-11, wherein replication of agiven transaction authorization request occurs in real-time withprocessing of the given transaction authorization request by thetransaction service provider system.

Clause 13: The system of any of Clause 8-12, wherein generating the atleast one shadow testing environment further comprises: identifying aset of computer resources available for operating shadow testingenvironments; determining a resource requirement of the at least onetesting policy; selecting, based at least partly on the resourcerequirement, a subset of computer resources to operate the at least oneshadow testing environment; and initiating the at least one shadowtesting environment using the subset of computer resources.

Clause 14: The system of any of Clause 8-13, wherein the server isfurther programmed and/or configured to, in response to detecting amodification of the at least one testing policy: determine, based on themodification, a new resource requirement of the at least one testingpolicy; select, based at least partly on the new resource requirement, anew subset of computer resources to operate the at least one shadowtesting environment; and initiate the at least one shadow testingenvironment using the new subset of computer resources.

Clause 15: A computer program product comprising at least onenon-transitory computer-readable medium including program instructionsthat, when executed by at least one processor, cause the at least oneprocessor to: store, in a database, at least one testing policycomprising an identifier of at least one machine-learning model and anidentifier of at least one transaction service; generate at least oneshadow testing environment operating the at least one transactionservice using the at least one machine-learning model; receive, at atransaction service provider system, at least one transactionauthorization request comprising transaction data of at least onetransaction associated with at least one payment device; identify the atleast one transaction service associated with the at least onetransaction based on a parameter of the transaction data; determine theat least one shadow testing environment associated with the at least onetransaction service; and replicate the transaction data in the at leastone shadow testing environment as input for testing the at least onetransaction service using the at least one machine-learning model.

Clause 16: The computer program product of Clause 15, wherein theprogram instructions further cause the at least one processor to: detecta modification of the at least one testing policy by a user through acomputer interface; determine that the modification comprises a newidentifier of at least one new machine-learning model and/or a newidentifier of at least one new transaction service; and regenerate theat least one shadow testing environment to operate the at least one newtransaction service and/or use the at least one new machine-learningmodel.

Clause 17: The computer program product of Clause 15 or 16, wherein theprogram instructions further cause the at least one processor to, inresponse to determining that the at least one new transaction serviceand/or the at least one new machine-learning model has not yet beenstored, prompt the user through the computer interface to provide the atleast one new transaction service and/or the at least one newmachine-learning model.

Clause 18: The computer program product of any of Clause 15-17, whereinthe program instructions further cause the at least one processor to:receive, at the transaction service provider system, a plurality oftransaction authorization requests comprising the at least onetransaction authorization request; and replicate, in a respective shadowtesting environment of the at least one shadow testing environment, eachof the plurality of transaction authorization requests that isassociated with a transaction service of the at least one testingpolicy.

Clause 19: The computer program product of any of Clause 15-18, whereingenerating the at least one shadow testing environment furthercomprises: identifying a set of computer resources available foroperating shadow testing environments; determining a resourcerequirement of the at least one testing policy; selecting, based atleast partly on the resource requirement, a subset of computer resourcesto operate the at least one shadow testing environment; and initiatingthe at least one shadow testing environment using the subset of computerresources.

Clause 20: The computer program product of any of Clause 15-19, whereinthe program instructions further cause the at least one processor to, inresponse to detecting a modification of the at least one testing policy:determine, based on the modification, a new resource requirement of theat least one testing policy; select, based at least partly on the newresource requirement, a new subset of computer resources to operate theat least one shadow testing environment; and initiate the at least oneshadow testing environment using the new subset of computer resources.

These and other features and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structures and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the presentdisclosure. As used in the specification and the claims, the singularform of “a,” “an,” and “the” include plural referents unless the contextclearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

Additional advantages and details of the disclosure are explained ingreater detail below with reference to the exemplary embodiments thatare illustrated in the accompanying schematic figures, in which:

FIG. 1 is a schematic diagram of one embodiment or aspect of a systemand method for operating dynamic shadow testing environments;

FIG. 2 is a flow diagram of one embodiment or aspect of a system andmethod for operating dynamic shadow testing environments;

FIG. 3 is a process diagram of one embodiment or aspect of a system andmethod for operating dynamic shadow testing environments;

FIG. 4 is a process diagram of one embodiment or aspect of a system andmethod for operating dynamic shadow testing environments; and

FIG. 5 is a process diagram of one embodiment or aspect of a system andmethod for operating dynamic shadow testing environments.

DETAILED DESCRIPTION

For purposes of the description hereinafter, the terms “upper”, “lower”,“right”, “left”, “vertical”, “horizontal”, “top”, “bottom”, “lateral”,“longitudinal,” and derivatives thereof shall relate to non-limitingembodiments as they are oriented in the drawing figures. However, it isto be understood that non-limiting embodiments may assume variousalternative variations and step sequences, except where expresslyspecified to the contrary. It is also to be understood that the specificdevices and processes illustrated in the attached drawings, anddescribed in the following specification, are simply exemplaryembodiments. Hence, specific dimensions and other physicalcharacteristics related to the embodiments disclosed herein are not tobe considered as limiting.

No aspect, component, element, structure, act, step, function,instruction, and/or the like used herein should be construed as criticalor essential unless explicitly described as such. Also, as used herein,the articles “a” and “an” are intended to include one or more items andmay be used interchangeably with “one or more” and “at least one.”Furthermore, as used herein, the term “set” is intended to include oneor more items (e.g., related items, unrelated items, a combination ofrelated and unrelated items, etc.) and may be used interchangeably with“one or more” or “at least one.” Where only one item is intended, theterm “one” or similar language is used. Also, as used herein, the terms“has,” “have,” “having,” or the like are intended to be open-endedterms. Further, the phrase “based on” is intended to mean “based atleast partly on” unless explicitly stated otherwise.

Some non-limiting embodiments are described herein in connection withthresholds. As used herein, satisfying a threshold may refer to a valuebeing greater than the threshold, more than the threshold, higher thanthe threshold, greater than or equal to the threshold, less than thethreshold, fewer than the threshold, lower than the threshold, less thanor equal to the threshold, equal to the threshold, and/or the like.

As used herein, the terms “communication” and “communicate” may refer tothe reception, receipt, transmission, transfer, provision, and/or thelike, of information (e.g., data, signals, messages, instructions,commands, and/or the like). For one unit (e.g., a device, a system, acomponent of a device or system, combinations thereof, and/or the like)to be in communication with another unit means that the one unit is ableto directly or indirectly receive information from and/or transmitinformation to the other unit. This may refer to a direct or indirectconnection (e.g., a direct communication connection, an indirectcommunication connection, and/or the like) that is wired and/or wirelessin nature. Additionally, two units may be in communication with eachother even though the information transmitted may be modified,processed, relayed, and/or routed between the first and second unit. Forexample, a first unit may be in communication with a second unit eventhough the first unit passively receives information and does notactively transmit information to the second unit. As another example, afirst unit may be in communication with a second unit if at least oneintermediary unit (e.g., a third unit located between the first unit andthe second unit) processes information received from the first unit andcommunicates the processed information to the second unit. In somenon-limiting embodiments, a message may refer to a network packet (e.g.,a data packet, and/or the like) that includes data. Any known electroniccommunication protocols and/or algorithms may be used such as, forexample, TCP/IP (including HTTP and other protocols), WLAN (including802.11 and other radio frequency-based protocols and methods), analogtransmissions, cellular networks (e.g., Global System for MobileCommunications (GSM), Code Division Multiple Access (CDMA), Long-TermEvolution (LTE), Worldwide Interoperability for Microwave Access(WiMAX®), etc.), and/or the like. It will be appreciated that numerousother arrangements are possible.

As used herein, the term “mobile device” may refer to one or moreportable electronic devices configured to communicate with one or morenetworks. As an example, a mobile device may include a cellular phone(e.g., a smartphone or standard cellular phone), a portable computer(e.g., a tablet computer, a laptop computer, etc.), a wearable device(e.g., a watch, pair of glasses, lens, clothing, and/or the like), apersonal digital assistant (PDA), and/or other like devices. The term“client device,” as used herein, refers to any electronic device that isconfigured to communicate with one or more servers or remote devicesand/or systems. A client device may include a mobile device, anetwork-enabled appliance (e.g., a network-enabled television,refrigerator, thermostat, and/or the like), a computer, a POS system,and/or any other device or system capable of communicating with anetwork.

As used herein, the term “computing device” may refer to one or moreelectronic devices that are configured to directly or indirectlycommunicate with or over one or more networks. The computing device maybe a mobile device. As an example, a mobile device may include acellular phone (e.g., a smartphone or standard cellular phone), aportable computer, a wearable device (e.g., watches, glasses, lenses,clothing, and/or the like), a personal digital assistant (PDA), and/orother like devices. The computing device may not be a mobile device,such as a desktop computer. Furthermore, the term “computer” may referto any computing device that includes the necessary components toreceive, process, and output data, and normally includes a display, aprocessor, a memory, an input device, and a network interface. An“application” or “application program interface” (API) refers tocomputer code or other data sorted on a computer-readable medium thatmay be executed by a processor to facilitate the interaction betweensoftware components, such as a client-side front-end and/or server-sideback-end for receiving data from the client. An “interface” refers to agenerated display, such as one or more graphical user interfaces (GUIs)with which a user may interact, either directly or indirectly (e.g.,through a keyboard, mouse, etc.).

As used herein, the term “payment device” may refer to a portablefinancial device, an electronic payment device, a payment card (e.g., acredit or debit card), a gift card, a smartcard, smart media, a payrollcard, a healthcare card, a wrist band, a machine-readable mediumcontaining account information, a keychain device or fob, an RFIDtransponder, a retailer discount or loyalty card, a cellular phone, anelectronic wallet mobile application, a personal digital assistant(PDA), a pager, a security card, a computer, an access card, a wirelessterminal, a transponder, and/or the like. In some non-limitingembodiments, the payment device may include volatile or non-volatilememory to store information (e.g., an account identifier, a name of theaccount holder, and/or the like).

As used herein, the term “transaction service provider” may refer to anentity that receives transaction authorization requests from merchantsor other entities and provides guarantees of payment, in some casesthrough an agreement between the transaction service provider and anissuer institution. For example, a transaction service provider mayinclude a payment network such as Visa® or any other entity thatprocesses transactions. The term “transaction processing system” mayrefer to one or more computer systems operated by or on behalf of atransaction service provider, such as a transaction processing serverexecuting one or more software applications, a token service executingone or more software applications, and/or the like. A transactionprocessing server may include one or more processors and, in somenon-limiting embodiments, may be operated by or on behalf of atransaction service provider.

As used herein, the term “issuer institution” may refer to one or moreentities, such as a bank, that provide accounts to customers forconducting transactions (e.g., payment transactions), such as initiatingcredit and/or debit payments. For example, an issuer institution mayprovide an account identifier, such as a primary account number (PAN),to a customer that uniquely identifies one or more accounts associatedwith that customer. The account identifier may be embodied on a paymentdevice, such as a physical payment instrument, e.g., a payment card,and/or may be electronic and used for electronic payments. The term“issuer system” refers to one or more computer systems operated by or onbehalf of an issuer institution, such as a server computer executing oneor more software applications. For example, an issuer system may includeone or more authorization servers for authorizing a transaction.

As used herein, the term “acquirer institution” may refer to an entitylicensed and/or approved by the transaction service provider tooriginate transactions (e.g., payment transactions) using a paymentdevice associated with the transaction service provider. Thetransactions the acquirer institution may originate may include paymenttransactions (e.g., purchases, original credit transactions (OCTs),account funding transactions (AFTs), and/or the like). In somenon-limiting embodiments, an acquirer institution may be a bank. As usedherein, the term “acquirer system” may refer to one or more computersystems, computer devices, software applications, and/or the likeoperated by or on behalf of an acquirer institution.

As used herein, the terms “authenticating system” and “authenticationsystem” may refer to one or more computing devices that authenticate auser and/or an account, such as but not limited to a transactionprocessing system, merchant system, issuer system, payment gateway, athird-party authenticating service, and/or the like.

As used herein, the term “account identifier” may include one or morePANs, tokens, or other identifiers associated with a customer account.The term “token” may refer to an identifier that is used as a substituteor replacement identifier for an original account identifier, such as aPAN. Account identifiers may be alphanumeric or any combination ofcharacters and/or symbols. Tokens may be associated with a PAN or otheroriginal account identifier in one or more data structures (e.g., one ormore databases and/or the like) such that they may be used to conduct atransaction without directly using the original account identifier. Insome examples, an original account identifier, such as a PAN, may beassociated with a plurality of tokens for different individuals orpurposes.

As used herein, the term “merchant” may refer to an individual or entitythat provides goods and/or services, or access to goods and/or services,to customers based on a transaction, such as a payment transaction. Theterm “merchant” or “merchant system” may also refer to one or morecomputer systems operated by or on behalf of a merchant, such as aserver computer executing one or more software applications. A“point-of-sale (POS) system,” as used herein, may refer to one or morecomputers and/or peripheral devices used by a merchant to engage inpayment transactions with customers, including one or more card readers,near-field communication (NFC) receivers, RFID receivers, and/or othercontactless transceivers or receivers, contact-based receivers, paymentterminals, computers, servers, input devices, and/or other like devicesthat can be used to initiate a payment transaction.

As used herein, the term “server” or “server computer” may refer to orinclude one or more processors or computers, storage devices, or similarcomputer arrangements that are operated by or facilitate communicationand processing for multiple parties in a network environment, such asthe Internet, although it will be appreciated that communication may befacilitated over one or more public or private network environments andthat various other arrangements are possible. Further, multiplecomputers, e.g., servers, or other computerized devices, e.g., POSdevices, directly or indirectly communicating in the network environmentmay constitute a “system,” such as a merchant's POS system. Reference to“a server” or “a processor,” as used herein, may refer to apreviously-recited server and/or processor that is recited as performinga previous step or function, a different server and/or processor, and/ora combination of servers and/or processors. For example, as used in thespecification and the claims, a first server and/or a first processorthat is recited as performing a first step or function may refer to thesame or different server and/or a processor recited as performing asecond step or function.

Non-limiting embodiments or aspects of the present disclosure aredirected to a system, method, and computer program product for operatingdynamic shadow testing environments. The described arrangement ofnetwork architecture and components therein is configured to provide anenhanced means of integrating platform shadowing and model shadowingseamlessly, with little to no effect on a production environment. Thedescribed system and methods can dynamically provision and shrink shadowenvironments to use computer resources more efficiently. Modificationsto testing policies may be detected in real-time with changes,triggering automatic and optimized reprovisioning of computer resources.Furthermore, the described systems and methods can execute shadowtesting with minimal manual input, optionally requiring user input ofnew service versions and/or new models into a shadow deploymentrepository and definition of shadowed testing policy. Live data may bemirrored in one or more shadow testing environments simultaneously, orsubstantially simultaneously, with the processing of the live data,providing a pseudo-production environment for testing services andmodels. Such an implementation improves accuracy of testing ofcomputer-implemented services, and decreases the security and data lossrisks associated of testing services on live data.

With specific reference to FIG. 1, and in some non-limiting embodimentsor aspects, provided is a system 100 for operating dynamic shadowtesting environments. The system 100 includes a shadow testingenvironment system, including one or more servers, for generating,operating, managing, and evaluating one or more shadow testingenvironments 116. A shadow testing environment may include softwareconfigured to test a process in a virtual environment, as if the testedprocess were operating on live data. Shadow testing environments may beoperated using one or more computing resources 114. A computer resource114 may include one or more processors and/or data storage mediums foroperating a shadow testing environment. Multiple shadow testingenvironments 116 may use the same or different computer resources 114.

The system 100 also includes a transaction service provider system 104.The transaction service provider system 104 may receive transaction dataof one or more transactions completed by payment devices 120 of one ormore payment device holders 118. The shadow testing environment system102 may partially or completely include the transaction service providersystem 104, and the transaction service provider system 104 maypartially or completely include the shadow testing environment system102. The transaction service provider system 104 may identifytransactions, based on stored testing policies 108, during processing ofcorresponding transaction authorization requests, to be replicated inone or more shadow testing environments 116. The shadow testingenvironment system 102 may also receive transaction data from thetransaction service provider system 104 and determine one or moretransactions to be replicated in one or more shadow testing environments116. A shadow testing environment 116 and/or testing policy 108 may beidentified as associated with a given transaction based on one or moreparameters of transaction data (e.g., payment device identifier, paymentdevice holder identifier, transaction amount, transaction time,transaction type, payment device type, merchant identifier, merchanttype, transaction description, and/or the like) compared to one or moreparameters of a testing policy 108, including transaction service 110,machine-learning model 112, triggering values or ranges of transactiondata, and/or the like.

The transaction service provider system 104 and/or the shadow testingenvironment system 102 may be communicatively connected to a policydatabase 106. The policy database 106 may be programmed and/orconfigured to store one or more testing policies 108. A testing policy108 may be a stored data record associated with a transaction service110 to be tested in a shadow testing environment 116. In somenon-limiting embodiments or aspects, if an actively running version of aservice is version 1.1, and new versions 1.2 and 1.3 are intended to betested on 70% and 30% of actively processed data, respectively, then thetesting policy may be defined as: {“active”: {“1 .1”:100}, “test”:[{“1.2”:70}, {“1.3”:30}]}. Transaction services 110 may include anycomputer-implemented service with one or more actions triggered by oneor more parameters of a transaction, including, but not limited to,fraud detection services, advertising services, consumer alert services,reward services, credit services, behavioral biometrics services,orchestration services, aggregation services, profile services,transformation services, model services, model serving services,decision services, and/or the like. Transaction services 110 may becarried out at least partly by one or more machine-learning models 112.Testing policies 108 may be stored in a policy database 106 in relationto one or more identifiers of transaction services 110 and one or moreidentifiers of machine-learning models 112. Transaction services 110 maybe delineated by one or more versions of the same transaction service110, and may be differentiated by one or more parameters, includingassociated machine-learning model 112. The policy database 106 oranother database may be used to store the code/script for executingtransaction services 110 using machine-learning models 112. In somenon-limiting embodiments or aspects, machine-learning models 112 mayinclude, but are not limited to, Smart Stand-in-Processing (STIP)models, Visa Deep Authorization (VDA) models, and/or the like.Performance data of a testing policy 108 in a shadow testing environment116 may be stored in the policy database 106 or another database.

A user 122 may interact with a computer interface 124 communicativelyconnected to the shadow testing environment system 102 to input one ormore testing policies 108 into the policy database 106. A user 122 mayview, edit, add, and/or delete testing policies 108 stored in the policydatabase 106, and individual users 122 may have variably presetpermissions for such actions. The user 122 may modify a testing policy108, such as by modifying an identifier of a transaction service 110 tobe tested and/or an identifier of a machine-learning model 112. Anidentifier of a transaction service 110 may be specific to a version ofa transaction service 110. The shadow testing environment system 102 maydetect a modification of the testing policy 108, and in particular, amodification of the identifiers of transaction service 110 andmachine-learning model 112. If the shadow testing environment system 102has access to the code/script for executing a transaction service 110using a machine-learning model 112 in a shadow testing environment 116,the shadow testing environment system 102 may proceed to generate ashadow testing environment 116 after a testing policy 108 isinput/modified. If the code/script for executing a transaction service110 and/or machine-learning model 112 was not previously stored, theshadow testing environment system 102 may prompt a user 122 (e.g., theuser 122 that modified the relevant testing policy 108) via a computerinterface 124 to provide the missing transaction service and/ormachine-learning model implementation code/script.

A testing policy 108 may further be associated with a time period forexecution in shadow testing environments. The shadow testing environmentsystem 102 may automatically generate one or more shadow testingenvironments 116 for a given testing policy 108 at the start of a timeperiod, and may halt a shadow testing environment 116 at the start of atime period. Performance data of a testing policy 108 during a timeperiod may be stored in the policy database 106 or another database.

The shadow testing environment system 102 may identify one or morecomputer resources 114 available for operating shadow testingenvironments 116. An allocation of available computer resources 114 maybe predefined and static, or an allocation of available computerresources 114 may be dynamic and determined based on parameters such asconnectivity, network usage, reliability, latency, and the like. Theshadow testing environment system 102 may determine a resourcerequirement of a given testing policy 108. Resource requirements may bedetermined from, but are not limited to, central processing unit (CPU)requirements, graphics processing unit (GPU) requirements, in-memorycaching requirements, and/or the like. A resource requirement mayinclude an estimate of processor memory, process speed, data storage,latency, and/or the like based on one or more parameters of a testingpolicy 108. For example, a testing policy 108 may be associated with afraud detection transaction service 110 that requires a convolutionalneural network machine-learning model 112, which may need a training setof 100,000 transactions of a given type and a testing set of 200,000transactions of a same type over a two-week sample time period. Based onsuch testing policy 108 parameters, the shadow testing environmentsystem 102 may select a subset of computer resources 114 havingsufficient processing, communication, and data storage capacity foroperating a shadow testing environment 116 running the associatedtransaction service 110, as if the transaction service 110 wereevaluating live transaction data and not replicated transaction data inthe shadow testing environment 116. Once a subset of computer resources114 is selected, the shadow testing environment system 102 may initiatethe shadow testing environment 116, begin replicating transaction dataassociated with the testing policy 108, and recording performance dataof the testing policy's 108 transaction service 110 and machine-learningmodel 112.

It will further be appreciated that while a shadow testing environment116 is in operation for a given testing policy 108, said testing policy108 may be modified by a user 122 to use a new version of a transactionservice 110, a new machine-learning model 112, new parameters forreplicated transaction data, and/or the like. The shadow testingenvironment system 102 may detect the modification to the testing policy108 and determine a new resource requirement of the updated testingpolicy 108. The shadow testing environment system 102 may select a newsubset of computer resources 114 based on the updated testing policy 108and initiate, e.g., redeploy, the shadow testing environment 116 to testthe updated testing policy 108.

With specific reference to FIG. 2, and in some non-limiting embodimentsor aspects, provided is a replication process 200 for operating dynamicshadow testing environments. The replication process 200 may be executedby a shadow testing environment system, a transaction service providersystem, another server, or any combination thereof. In step 202, thetransaction service provider system may receive a transactionauthorization request for a transaction. Transaction data of thetransaction may be forwarded to the shadow testing environment system.The shadow testing environment system may evaluate one or moreparameters of the transaction data to determine if the transactionshould be replicated in one or more shadow testing environments. Testingpolicies may be selected based on one or more associated transactioncharacteristics, including, but not limited to, transaction type,transaction location, transaction amount, transaction data, merchantidentifier, merchant type, and/or the like. For a transaction that isassociated with at least one testing policy, the shadow testingenvironment system may carry out model selection in step 204. Based onthe testing policy (e.g., machine-learning model identifier) and/or oneor more transaction data parameters, the shadow testing environmentsystem may identify an associated machine learning model 206. After aselected model 208 is identified, the shadow testing environment systemmay select a version of a transaction service in step 210. Based on thetesting policy (e.g., transaction service identifier) and/or one or moretransaction data parameters, the shadow testing environment system mayidentify a transaction service 212. After a selected service 214 isidentified, the shadow testing environment system may process thetransaction into a shadow testing environment in step 216.

If a shadow testing environment for a given selected model 208 andselected service 214 has already been initiated, transaction data of atriggering request 202 may be replicated in the shadow testingenvironment. If the transaction is associated with a selected model 208and selected service 214 for which no shadow testing environment isoperating, the shadow testing environment system may initiate a newshadow testing environment. While the aforementioned replication process200 includes a step-by-step identification of a selected model 208 andselected service 214 prior to processing into a shadow environment 216,it will be appreciated that other parameters besides model and servicemay be used for associating transactions with testing policies and/orshadow testing environments.

With specific reference to FIG. 3, and in some non-limiting embodimentsor aspects, provided is a method 300 for operating dynamic shadowtesting environments. One or more steps of the method 300 may beexecuted by a shadow testing environment system, transaction serviceprovider system, another server, or a combination thereof. In step 302,the shadow testing environment system may store one or more testingpolicies in a policy database. Testing policies may include identifiersof one or more machine-learning models and identifiers of one or moretransaction services. Testing policies may also include definitions(e.g., code, script, parameters, etc.) of machine-learning models and/ortransaction services. In step 304, the shadow testing environment systemmay generate one or more shadow testing environments using one or morecomputer resources. A shadow testing environment operates one or moretransaction services using one or more machine-learning models. In step306, the transaction service provider system may receive one or moretransaction authorization requests including transaction data of one ormore transactions associated with one or more payment devices. Forexample, when a payment device interacts with a merchant point-of-saleterminal to complete a transaction, a transaction authorization requestis generated and sent to the transaction service provider system forprocessing of the transaction. Transaction data may include transactionidentifier, transaction amount, transaction type, transaction time,merchant type, merchant identifier, merchant location, payment deviceidentifier, user identifier, transaction description, and/or the like.Steps 306 to 312 may be executed for a plurality of transactionauthorization requests, and data of one or more transactions of theplurality of transaction authorization requests may be replicated inrespective shadow testing environments based on associated transactionservices, machine-learning models, and/or testing policies associatedwith said transactions. Furthermore, replication, in step 312, may occurin real-time with processing a given transaction authorization requestby the transaction service provider system.

In step 308, the shadow testing environment system may identify one ormore transaction services associated with one or more transactions basedon a parameter of the transaction data. For example, the transaction maybe for a credit transaction and an amount satisfying a threshold set byan issuer of the payment device used for the transaction. Such atransaction may implicate a fraud detection transaction service that isdesignated in a stored testing policy. The shadow testing environmentsystem may additionally, or alternatively, identify a machine-learningmodel associated with the transaction or a testing policy associatedwith the transaction based on one or more transaction parameters. Instep 310, the shadow testing environment system may determine a shadowtesting environment associated with the transaction service. The shadowtesting environment may additionally or alternatively identify a shadowtesting environment associated with a machine-learning model and/ortesting policy. A determined shadow testing environment may be generatedin response to the identification of a relevant transaction, or a shadowtesting environment may already be running at the time of receipt of atransaction. In step 312, shadow testing environment system mayreplicate a portion or all of transaction data in a shadow testingenvironment as an input for testing the one or more transaction servicesusing the one or more machine-learning models. For example, thetransaction data may be replicated in a shadow testing environment for afraud detection transaction service, which may appear to the transactionservice to be a live transaction being processed, and which may triggerone or more fraud prevention countermeasures in the testbed of theshadow testing environment. In such a way, transaction services may betested and evaluated without interacting with live data or taking liveactions while a transaction service has not yet been fully tested.

With specific reference to FIG. 4, and in some non-limiting embodimentsor aspects, provided is a method 400 for operating dynamic shadowtesting environments. One or more steps of the method 400 may beexecuted by a shadow testing environment system, a transaction serviceprovider system, another server, or a combination thereof. In step 402,the shadow testing environment system may detect one or moremodifications of one or more stored testing policies. In step 404, theshadow testing environment system may determine that the modificationincludes a new identifier of one or more machine-learning models and/ora new identifier of one or more transaction services. It will beappreciated that in the case of a plurality of models or services, aplurality of identifiers may be modified. Alternatively, oradditionally, definitions (e.g., code, script, parameters, etc.) of oneor more machine-learning models and/or one or more transaction servicesmay be modified. Modifications may be executed by a user (e.g.,management personnel) interacting with the shadow testing environmentsystem via a computer interface (e.g., a web portal executing anapplication programming interface).

In step 406, the shadow testing environment system may determine if oneor more new machine-learning models and/or transaction services,identified by new identifiers or definitions, are implicated, and if so,if said new model(s) or new service(s) were previously stored. Step 406may additionally, or alternatively, determine if one or more new modelsor services are accessible by a processor of the shadow testingenvironment system. If one or more new machine-learning models and/ortransaction services are not stored or available for retrieval, theshadow testing environment system may, in step 408, prompt a user via acomputer interface for the one or more new machine-learning modelsand/or transaction services. The user may then use the computerinterface to identify the storage location of the one or more new modelsand/or services. The user may also use the computer interface toupload/communicate the definitions (e.g., code, script, parameters,etc.) of one or more new models and/or services. Once one or more newmodels and/or services are stored and available, either originally afterstep 406 or after prompting a user in step 408, the shadow testingenvironment system may regenerate one or more shadow testingenvironments to operate the one or more new machine-learning modelsand/or transaction services. Steps 402 to 410 may be carried out whilenew transactions are continually processed at a transaction serviceprovider system, and may dynamically allocate regenerated shadow testingenvironments to computer resources according to resource availabilityand requirements.

With specific reference to FIG. 5, and in some non-limiting embodimentsor aspects, provided is a method 500 for operating dynamic shadowtesting environments. One or more steps of method 500 may be executed bya shadow testing environment system, a transaction service providersystem, another server, or a combination thereof. In step 502, theshadow testing environment system may identify a set of computerresources available for operating shadow testing environments. The setof computer resources may be one or more computer resources available tothe shadow testing environment system, which may be allocated in wholeor part to one or more shadow testing environments. In step 504, theshadow testing environment system may determine a resource requirement(e.g., processing memory, data storage, bit rate, latency, uptime, etc.)for each or all of one or more testing policies. In step 506, the shadowtesting environment system may, based on the determined resourcerequirement, select a subset of computer resources to operate the one ormore shadow testing environments. The subset of computer resources maybe part or all of the set of computer resources. In step 508, the shadowtesting environment system may initiate one or more shadow testingenvironments using the subset of computer resources. Any given computerresource may or may not be used for more than one shadow testingenvironment, and any given shadow testing environment may require one ormore computer resources.

In step 510, the shadow testing environment system may detect amodification of one or more testing policy, which may include a changeto identifiers or definitions (e.g., code, script, parameters) of one ormore machine-learning models and/or transaction services. In response tothe detection, the shadow testing environment may repeat steps 504 to508 to determine a new resource requirement of the modified testingpolicy, select a new subset of computer resources to operate one or moreshadow testing environments of the modified testing policy, and initiate(e.g., restart, regenerate, etc.) the one or more shadow testingenvironments on the new subset of computer resources. It will beappreciated that a new resource requirement may be the same or differentfrom a previous resource requirement. It will also be appreciated that anew subset of computer resources may be the same or different from aprevious selected subset of computer resources. Steps 510 and 504 to 508may be repeated upon detection of a modification of one or more testingpolicies.

With further reference to the foregoing figures, and in furthernon-limiting embodiments or aspects, consider the following exampleexecution workflow. A user, such as a management personnel, may access acomputing device and input their security credentials to view a computerinterface for communicating with a shadow testing environment system.The user may interact with a series of web forms to define a new testingpolicy having a specific version of transaction service, such as a frauddetection service, that is operated using a specific machine-learningmodel, such as a behavioral biometrics model. The shadow testingenvironment system may store the parameters of the testing policy in apolicy database, which may include identifiers and/or definitions of themodel and service. The definitions (e.g., code, script, etc.) of thefraud detection service and/or behavioral biometrics model may be storedin the same database or a separate database to act as a definitionrepository.

The shadow testing environment system may then identify a resourcerequirement of the input testing policy. The testing policy and/ordefinitions of the service and/or model, for example, may haveparameters specifying card-not-present transaction type, for creditpayment devices, for amounts greater than $10, completed at a timewithin an hour of a prior transaction of the payment device, over a onemonth time period. Based on one or more parameters of the testingpolicy, service, and/or model, the shadow testing environment system mayidentify and allocate one or more computer resources for the executionof the service and model. For example, the shadow testing environmentmay determine a data storage requirement of 20 TB, an input raterequirement of 10 Gb, and a processing requirement of 128 GB of RAMdistributed over 32 processor cores. Based on the determined resourcerequirement, the shadow testing environment may allocate one or morecomputer resources (e.g., data storage and processor server arrays) ofthe total available computer resources to run the new shadow testingenvironment. After computer resources are allocated, the shadow testingenvironment may be initiated and the service and model of thecorresponding testing policy may be activated, where testing may begin.

As live transaction data is received at a transaction service providersystem, the transaction service provider system and/or shadow testingenvironment system may determine if any given transaction is relevant toa testing policy being run, or intended to be run, on a shadow testingenvironment. For the above example fraud detection service, a newtransaction may satisfy one or more parameters of the testing policy,service, and/or model, which may identify the transaction as relevant tothe testing policy. The transaction data may then be replicated in theoperating shadow testing environment, as if the transaction data waslive production data. In such a manner, the fraud detection servicebeing tested may interact with the replicated transaction data andtrigger actions in the shadow testing environment.

With further reference to the foregoing figures, and in furthernon-limiting embodiments or aspects, the shadow testing environmentsystem may include a number of program layers for executing thedescribed steps. For example, the shadow testing environment system mayinclude a control layer for defining platform and model experimentationpolicies. The shadow testing environment system may include a resourceprovisioning service to create experimentation environments based ontesting policies. The shadow testing environment system may include apolicy execution engine to add code/script (e.g., http headers) forindividual transactions and replicate transactions in shadow testingenvironments based on testing policies. The shadow testing environmentsystem may also include a data routing layer to forward the originaland/or replicated transactions to services and models. It will furtherbe appreciated that while the services described above have beendescribed principally in the transaction context, the systems andmethods for generating and operating shadow testing environments may beuseful for non-transaction based live data flows, services,machine-learning models, and testing policies.

Although the disclosure has been described in detail for the purpose ofillustration based on what is currently considered to be the mostpractical and non-limiting embodiments, it is to be understood that suchdetail is solely for that purpose and that the disclosure is not limitedto the disclosed embodiments, but, on the contrary, is intended to covermodifications and equivalent arrangements that are within the spirit andscope of the appended claims. For example, it is to be understood thatthe present disclosure contemplates that, to the extent possible, one ormore features of any embodiment can be combined with one or morefeatures of any other embodiment.

What is claimed is:
 1. A computer-implemented method comprising:generating, with at least one processor, at least one shadow testingenvironment operating at least one transaction service and using atleast one machine-learning model, the at least one transaction servicehaving at least one action triggered by one or more parameters of apayment transaction; receiving, with the at least one processor at atransaction processing system of a transaction service provider in anelectronic payment processing network, a plurality of transactionauthorization requests, each transaction authorization request of theplurality of transaction authorization requests comprising transactiondata of a payment transaction associated with a payment device, whereinthe transaction processing system is configured to receive the pluralityof transaction authorization requests from at least one merchant andprocess payment transactions associated with the plurality oftransaction authorization requests as said payment transactions areinitiated by at least one point-of-sale terminal of the at least onemerchant; determining, with the at least one processor, a respectiveshadow testing environment of the at least one shadow testingenvironment associated with each transaction authorization request ofthe plurality of transaction authorization requests; and replicating,with the at least one processor in real-time with processing payment foreach payment transaction associated with each transaction authorizationrequest of the plurality of transaction authorization requests, thetransaction data of said each transaction authorization request in therespective shadow testing environment associated with said eachtransaction authorization request to produce replicated transactiondata, wherein the replicated transaction data is used as input fortesting the at least one transaction service.
 2. The method of claim 1,further comprising storing, with the at least one processor and in adatabase, at least one testing policy comprising an identifier of the atleast one machine-learning model and an identifier of the at least onetransaction service.
 3. The method of claim 2, further comprising:detecting, with the at least one processor, a modification of the atleast one testing policy by a user through a computer interface;determining, with the at least one processor, that the modificationcomprises at least one of a new identifier of at least one newmachine-learning model and a new identifier of at least one newtransaction service; and regenerating, with the at least one processor,the at least one shadow testing environment to perform at least one ofthe following: operate the at least one new transaction service, and usethe at least one new machine-learning model.
 4. The method of claim 3,further comprising, in response to determining that at least one of theat least one new transaction service and the at least one newmachine-learning model has not yet been stored, prompting, with the atleast one processor, the user through the computer interface to provideat least one of the at least one new transaction service and the atleast one new machine-learning model.
 5. The method of claim 1, whereindetermining the respective shadow testing environment of the at leastone shadow testing environment associated with each transactionauthorization request of the plurality of transaction authorizationrequests further comprises identifying a transaction service associatedwith a parameter of the transaction data of said each transactionauthorization request.
 6. The method of claim 2, wherein generating theat least one shadow testing environment further comprises: identifying,with the at least one processor, a set of computer resources availablefor operating shadow testing environments; determining, with the atleast one processor, a resource requirement of the at least one testingpolicy; selecting, with the at least one processor based at least partlyon the resource requirement, a subset of computer resources to operatethe at least one shadow testing environment; and initiating, with the atleast one processor, the at least one shadow testing environment usingthe subset of computer resources.
 7. The method of claim 6, furthercomprising, in response to detecting a modification of the at least onetesting policy: determining, with the at least one processor and basedon the modification, a new resource requirement of the at least onetesting policy; selecting, with the at least one processor based atleast partly on the new resource requirement, a new subset of computerresources to operate the at least one shadow testing environment; andinitiating, with the at least one processor, the at least one shadowtesting environment using the new subset of computer resources.
 8. Asystem comprising a server comprising at least one processor, the serverbeing at least one of programmed and configured to: generate at leastone shadow testing environment operating at least one transactionservice and using at least one machine-learning model, the at least onetransaction service having at least one action triggered by one or moreparameters of a payment transaction; receive, at a transactionprocessing system of a transaction service provider in an electronicpayment processing network, a plurality of transaction authorizationrequests, each transaction authorization request of the plurality oftransaction authorization requests comprising transaction data of apayment transaction associated with a payment device, wherein thetransaction processing system is configured to receive the plurality oftransaction authorization requests from at least one merchant andprocess payment transactions associated with the plurality oftransaction authorization requests as said payment transactions areinitiated by at least one point-of-sale terminal of the at least onemerchant; determine a respective shadow testing environment of the atleast one shadow testing environment associated with each transactionauthorization request of the plurality of transaction authorizationrequests; and replicate, in real-time with processing payment for eachpayment transaction associated with each transaction authorizationrequest of the plurality of transaction authorization requests, thetransaction data of said each transaction authorization request in therespective shadow testing environment associated with said eachtransaction authorization request to produce replicated transactiondata, wherein the replicated transaction data is used as input fortesting the at least one transaction service.
 9. The system of claim 8,wherein the server is further at least one of programmed and configuredto store, in a database, at least one testing policy comprising anidentifier of the at least one machine-learning model and an identifierof the at least one transaction service.
 10. The system of claim 9,wherein the server is further at least one of programmed and configuredto: detect a modification of the at least one testing policy by a userthrough a computer interface; determine that the modification comprisesat least one of a new identifier of at least one new machine-learningmodel and a new identifier of at least one new transaction service; andregenerate the at least one shadow testing environment to perform atleast one of the following: operate the at least one new transactionservice, and use the at least one new machine-learning model.
 11. Thesystem of claim 10, wherein the server is further at least one ofprogrammed and configured to, in response to determining that at leastone of the at least one new transaction service and the at least one newmachine-learning model has not yet been stored, prompt the user throughthe computer interface to provide at least one of the at least one newtransaction service and the at least one new machine-learning model. 12.The system of claim 8, wherein determining the respective shadow testingenvironment of the at least one shadow testing environment associatedwith each transaction authorization request of the plurality oftransaction authorization requests further comprises identifying atransaction service associated with a parameter of the transaction dataof said each transaction authorization request.
 13. The system of claim9, wherein generating the at least one shadow testing environmentfurther comprises: identifying a set of computer resources available foroperating shadow testing environments; determining a resourcerequirement of the at least one testing policy; selecting, based atleast partly on the resource requirement, a subset of computer resourcesto operate the at least one shadow testing environment; and initiatingthe at least one shadow testing environment using the subset of computerresources.
 14. The system of claim 13, wherein the server is further atleast one of programmed and configured to, in response to detecting amodification of the at least one testing policy: determine, based on themodification, a new resource requirement of the at least one testingpolicy; select, based at least partly on the new resource requirement, anew subset of computer resources to operate the at least one shadowtesting environment; and initiate the at least one shadow testingenvironment using the new subset of computer resources.
 15. A computerprogram product comprising at least one non-transitory computer-readablemedium including program instructions that, when executed by at leastone processor, cause the at least one processor to: generate at leastone shadow testing environment operating at least one transactionservice and using at least one machine-learning model, the at least onetransaction service having at least one action triggered by one or moreparameters of a payment transaction; receive, at a transactionprocessing system of a transaction service provider-in an electronicpayment processing network, a plurality of transaction authorizationrequests, each transaction authorization request of the plurality oftransaction authorization requests comprising transaction data of apayment transaction associated with a payment device, wherein thetransaction processing system is configured to receive the plurality oftransaction authorization requests from at least one merchant andprocess payment transactions associated with the plurality oftransaction authorization requests as said payment transactions areinitiated by at least one point-of-sale terminal of the at least onemerchant; determine a respective shadow testing environment of the atleast one shadow testing environment associated with each transactionauthorization request of the plurality of transaction authorizationrequests; and replicate, in real-time with processing payment for eachpayment transaction associated with each transaction authorizationrequest of the plurality of transaction authorization requests, thetransaction data of said each transaction authorization request in therespective shadow testing environment associated with said eachtransaction authorization request to produce replicated transactiondata, wherein the replicated transaction data is used as input fortesting the at least one transaction service.
 16. The computer programproduct of claim 15, wherein the program instructions further cause theat least one processor to store, in a database, at least one testingpolicy comprising an identifier of the at least one machine-learningmodel and an identifier of the at least one transaction service.
 17. Thecomputer program product of claim 16, wherein the program instructionsfurther cause the at least one processor to: detect a modification ofthe at least one testing policy by a user through a computer interface;determine that the modification comprises at least one of a newidentifier of at least one new machine-learning model and a newidentifier of at least one new transaction service; and regenerate theat least one shadow testing environment to perform at least one of thefollowing: operate the at least one new transaction service, and use theat least one new machine-learning model.
 18. The computer programproduct of claim 15, wherein determining the respective shadow testingenvironment of the at least one shadow testing environment associatedwith each transaction authorization request of the plurality oftransaction authorization requests further comprises identifying atransaction service associated with a parameter of the transaction dataof said each transaction authorization request.
 19. The computer programproduct of claim 16, wherein generating the at least one shadow testingenvironment further comprises: identifying a set of computer resourcesavailable for operating shadow testing environments; determining aresource requirement of the at least one testing policy; selecting,based at least partly on the resource requirement, a subset of computerresources to operate the at least one shadow testing environment; andinitiating the at least one shadow testing environment using the subsetof computer resources.
 20. The computer program product of claim 19,wherein the program instructions further cause the at least oneprocessor to, in response to detecting a modification of the at leastone testing policy: determine, based on the modification, a new resourcerequirement of the at least one testing policy; select, based at leastpartly on the new resource requirement, a new subset of computerresources to operate the at least one shadow testing environment; andinitiate the at least one shadow testing environment using the newsubset of computer resources.